



'''simple docstring'''



import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    BioGptForCausalLM,
							    BioGptForSequenceClassification,
							    BioGptForTokenClassification,
							    BioGptModel,
							    BioGptTokenizer,
							)
							from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class 						lowerCamelCase_				:







						def __init__(	self :   str					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Any=13					,     lowerCAmelCase__ :   Optional[Any]=7					,     lowerCAmelCase__ :   Optional[Any]=True					,     lowerCAmelCase__ :   Any=True					,     lowerCAmelCase__ :   List[Any]=False					,     lowerCAmelCase__ :   List[Any]=True					,     lowerCAmelCase__ :   List[Any]=99					,     lowerCAmelCase__ :   Dict=32					,     lowerCAmelCase__ :   Tuple=5					,     lowerCAmelCase__ :   Any=4					,     lowerCAmelCase__ :   Any=37					,     lowerCAmelCase__ :   Union[str, Any]="gelu"					,     lowerCAmelCase__ :   Dict=0.1					,     lowerCAmelCase__ :   List[str]=0.1					,     lowerCAmelCase__ :   Optional[Any]=5_12					,     lowerCAmelCase__ :   Union[str, Any]=16					,     lowerCAmelCase__ :   int=2					,     lowerCAmelCase__ :   Tuple=0.02					,     lowerCAmelCase__ :   List[Any]=3					,     lowerCAmelCase__ :   List[str]=4					,     lowerCAmelCase__ :   List[Any]=None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      parent
													SCREAMING_SNAKE_CASE       :   Dict         =      batch_size
													SCREAMING_SNAKE_CASE       :   int         =      seq_length
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      is_training
													SCREAMING_SNAKE_CASE       :   List[Any]         =      use_input_mask
													SCREAMING_SNAKE_CASE       :   int         =      use_token_type_ids
													SCREAMING_SNAKE_CASE       :   Dict         =      use_labels
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      vocab_size
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      hidden_size
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      num_hidden_layers
													SCREAMING_SNAKE_CASE       :   Tuple         =      num_attention_heads
													SCREAMING_SNAKE_CASE       :   List[Any]         =      intermediate_size
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      hidden_act
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      hidden_dropout_prob
													SCREAMING_SNAKE_CASE       :   Any         =      attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE       :   str         =      max_position_embeddings
													SCREAMING_SNAKE_CASE       :   Any         =      type_vocab_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      type_sequence_label_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      initializer_range
													SCREAMING_SNAKE_CASE       :   int         =      num_labels
													SCREAMING_SNAKE_CASE       :   str         =      num_choices
													SCREAMING_SNAKE_CASE       :   List[Any]         =      scope







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size					)

													SCREAMING_SNAKE_CASE       :   int         =      None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      random_attention_mask([self.batch_size, self.seq_length]					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      None
													SCREAMING_SNAKE_CASE       :   Tuple         =      None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE       :   int         =      ids_tensor([self.batch_size]					,     self.type_sequence_label_size					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      ids_tensor([self.batch_size]					,     self.num_choices					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      self.get_config()

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return BioGptConfig(
													    vocab_size=self.vocab_size					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     max_position_embeddings=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     is_decoder=lowerCAmelCase__					,     initializer_range=self.initializer_range					,     )







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      BioGptModel(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(lowerCAmelCase__					)
													self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)					)







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Dict					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      BioGptForCausalLM(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     token_type_ids=lowerCAmelCase__					,     labels=lowerCAmelCase__					)
													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   int					,     *lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      BioGptModel(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()

													# create attention mask
													SCREAMING_SNAKE_CASE       :   str         =      torch.ones(input_ids.shape					,     dtype=torch.long					,     device=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      self.seq_length // 2
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0

													# first forward pass
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					).to_tuple()

													# create hypothetical next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE       :   Dict         =      ids_tensor((self.batch_size, 1)					,     config.vocab_size					)

													# change a random masked slice from input_ids
													SCREAMING_SNAKE_CASE       :   Any         =      ids_tensor((1,)					,     lowerCAmelCase__					).item() + 1
													SCREAMING_SNAKE_CASE       :   int         =      ids_tensor((self.batch_size, 1)					,     config.vocab_size					).squeeze(-1					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      random_other_next_tokens

													# append to next input_ids and attn_mask
													SCREAMING_SNAKE_CASE       :   Dict         =      torch.cat([input_ids, next_tokens]					,     dim=-1					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.cat(
													    [attn_mask, torch.ones((attn_mask.shape[0], 1)					,     dtype=torch.long					,     device=lowerCAmelCase__					)]					,     dim=1					,     )

													# get two different outputs
													SCREAMING_SNAKE_CASE       :   Tuple         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					,     past_key_values=lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					)['''last_hidden_state''']

													# select random slice
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ids_tensor((1,)					,     output_from_past.shape[-1]					).item()
													SCREAMING_SNAKE_CASE       :   str         =      output_from_no_past[:, -1, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE       :   Dict         =      output_from_past[:, 0, random_slice_idx].detach()

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__					,     lowerCAmelCase__					,     atol=1e-3					)					)







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   List[str]					,     *lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      BioGptModel(config=lowerCAmelCase__					).to(lowerCAmelCase__					).eval()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.ones(input_ids.shape					,     dtype=torch.long					,     device=lowerCAmelCase__					)

													# first forward pass
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     use_cache=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      outputs.to_tuple()

													# create hypothetical multiple next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE       :   List[str]         =      ids_tensor((self.batch_size, 3)					,     config.vocab_size					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      ids_tensor((self.batch_size, 3)					,     2					)

													# append to next input_ids and
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.cat([input_ids, next_tokens]					,     dim=-1					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.cat([attention_mask, next_attn_mask]					,     dim=-1					)

													SCREAMING_SNAKE_CASE       :   Dict         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     past_key_values=lowerCAmelCase__					)[
													    '''last_hidden_state'''
													]

													# select random slice
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      ids_tensor((1,)					,     output_from_past.shape[-1]					).item()
													SCREAMING_SNAKE_CASE       :   List[str]         =      output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      output_from_past[:, :, random_slice_idx].detach()

													self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__					,     lowerCAmelCase__					,     atol=1e-3					)					)







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   List[Any]					,     *lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[Any]=False					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      BioGptForCausalLM(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													SCREAMING_SNAKE_CASE       :   int         =      model(lowerCAmelCase__					,     labels=lowerCAmelCase__					)
													self.parent.assertEqual(result.loss.shape					,     ()					)
													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)					)
													result.loss.backward()







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   str					,     *lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      BioGptModel(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers					)
													for key in model.state_dict().keys():
																				if "c_proj" in key and "weight" in key:
																											self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]					) - model_std					)					,     0.001					)
																											self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]					) - 0.0					)					,     0.01					)







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   int					,     *lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.num_labels
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      BioGptForTokenClassification(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     token_type_ids=lowerCAmelCase__					)
													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.num_labels)					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,(
													    SCREAMING_SNAKE_CASE
													)       ,
													)       :   Union[str, Any]         =      config_and_inputs
													SCREAMING_SNAKE_CASE       :   str         =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase_				(       snake_case_     ,					snake_case_     ,					snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : int						  =					(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase  : Any						  =					(BioGptForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase  : List[str]						  =					(
						    {
						        'feature-extraction': BioGptModel,
						        'text-classification': BioGptForSequenceClassification,
						        'text-generation': BioGptForCausalLM,
						        'token-classification': BioGptForTokenClassification,
						        'zero-shot': BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase  : Union[str, Any]						  =					False







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      BioGptModelTester(self					)
													SCREAMING_SNAKE_CASE       :   Any         =      ConfigTester(self					,     config_class=lowerCAmelCase__					,     hidden_size=37					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      type
																				self.model_tester.create_and_check_model(*lowerCAmelCase__					)







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__					,     gradient_checkpointing=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__					)







						@slow
						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
													model.to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      '''left'''

													# Define PAD Token = EOS Token = 50256
													SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer.eos_token
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model.config.eos_token_id

													# use different length sentences to test batching
													SCREAMING_SNAKE_CASE       :   int         =      [
													    '''Hello, my dog is a little''',
													    '''Today, I''',
													]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tokenizer(lowerCAmelCase__					,     return_tensors='''pt'''					,     padding=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      inputs['''input_ids'''].to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.generate(
													    input_ids=lowerCAmelCase__					,     attention_mask=inputs['''attention_mask'''].to(lowerCAmelCase__					)					,     )

													SCREAMING_SNAKE_CASE       :   List[Any]         =      tokenizer(sentences[0]					,     return_tensors='''pt'''					).input_ids.to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model.generate(input_ids=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
													SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer(sentences[1]					,     return_tensors='''pt'''					).input_ids.to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      model.generate(input_ids=lowerCAmelCase__					,     max_length=model.config.max_length - num_paddings					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      tokenizer.batch_decode(lowerCAmelCase__					,     skip_special_tokens=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tokenizer.decode(output_non_padded[0]					,     skip_special_tokens=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer.decode(output_padded[0]					,     skip_special_tokens=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Dict         =      [
													    '''Hello, my dog is a little bit bigger than a little bit.''',
													    '''Today, I have a good idea of how to use the information''',
													]
													self.assertListEqual(lowerCAmelCase__					,     lowerCAmelCase__					)
													self.assertListEqual(lowerCAmelCase__					,     [non_padded_sentence, padded_sentence]					)







						@slow
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      BioGptModel.from_pretrained(lowerCAmelCase__					)
																				self.assertIsNotNone(lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      3
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE       :   List[str]         =      input_ids.ne(1					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size					)
													SCREAMING_SNAKE_CASE       :   int         =      BioGptForSequenceClassification(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     labels=lowerCAmelCase__					)
													self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)					)







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      3
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      '''multi_label_classification'''
													SCREAMING_SNAKE_CASE       :   Dict         =      input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE       :   Dict         =      input_ids.ne(1					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]					,     self.model_tester.type_sequence_label_size					).to(torch.float					)
													SCREAMING_SNAKE_CASE       :   str         =      BioGptForSequenceClassification(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Any         =      model(lowerCAmelCase__					,     attention_mask=lowerCAmelCase__					,     labels=lowerCAmelCase__					)
													self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)					)


@require_torch
class 						lowerCamelCase_				(       unittest.TestCase      ):







						@slow
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
													SCREAMING_SNAKE_CASE       :   str         =      torch.tensor([[2, 48_05, 9, 6_56, 21]]					)
													SCREAMING_SNAKE_CASE       :   Any         =      model(lowerCAmelCase__					)[0]

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      4_23_84

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.Size((1, 5, vocab_size)					)
													self.assertEqual(output.shape					,     lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.tensor(
													    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]					)

													self.assertTrue(torch.allclose(output[:, :3, :3]					,     lowerCAmelCase__					,     atol=1e-4					)					)







						@slow
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
													model.to(lowerCAmelCase__					)

													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   int         =      tokenizer('''COVID-19 is'''					,     return_tensors='''pt'''					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      model.generate(
													    **lowerCAmelCase__					,     min_length=1_00					,     max_length=10_24					,     num_beams=5					,     early_stopping=lowerCAmelCase__					,     )
													SCREAMING_SNAKE_CASE       :   Dict         =      tokenizer.decode(output_ids[0]					,     skip_special_tokens=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      (
													    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
													    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
													    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
													    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
													    ''' more than 800,000 deaths.'''
													)
													self.assertEqual(lowerCAmelCase__					,     lowerCAmelCase__					)









'''simple docstring'''



from collections import defaultdict
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      1
							SCREAMING_SNAKE_CASE       :   Dict         =      True
							for v in tree[start]:
														if v not in visited:
																					ret += dfs(A					)
							if ret % 2 == 0:
														cuts.append(A					)
							return ret
def        UpperCAmelCase (    ):
							dfs(1					)


if __name__ == "__main__":
							lowerCAmelCase_      ,				lowerCAmelCase_  :							Dict             =    10, 9
							lowerCAmelCase_  :							Dict             =    defaultdict(list)
							lowerCAmelCase_  :							dict[int, bool]             =    {}
							lowerCAmelCase_  :							list[int]             =    []
							lowerCAmelCase_  :							Optional[Any]             =    0
							lowerCAmelCase_  :							Dict             =    [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)




